A Functional Approach to Curve Alignment and Shape Analysis
Researchers have developed a new framework for analyzing shapes using Functional Data Analysis (FDA). This method uses basis expansion techniques to estimate deformation variables like scaling, translation, and rotation, allowing for curve alignment. A generative model for random contours is then created using principal component analysis. Experiments on simulated data and the MPEG-7 database show the framework's success in identifying deformation parameters and capturing contour distributions where traditional FDA methods fall short. AI